






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
   from PIL import Image




class 							_lowerCAmelCase(			UpperCAmelCase_   ):





       """simple docstring"""







       a							:						int										='Salesforce/blip-image-captioning-base'
       a							:						Dict										=(
           'This is a tool that generates a description of an image. It takes an input named `image` which should be the '
           'image to caption, and returns a text that contains the description in English.'
       )
       a							:						Optional[Any]										='image_captioner'
       a							:						Optional[int]										=AutoModelForVisionaSeq

       a							:						str										=['image']
       a							:						Tuple										=['text']





       def __init__(   self    ,     *_lowerCamelCase    ,     **_lowerCamelCase				):
         requires_backends(self    ,     ['vision']				)
         super().__init__(*_lowerCamelCase    ,     **_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return self.pre_processor(images=_lowerCamelCase    ,     return_tensors='pt'				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return self.model.generate(**_lowerCamelCase				)





       def      _a       (   self    ,     _lowerCamelCase				):
         return self.pre_processor.batch_decode(_lowerCamelCase    ,     skip_special_tokens=_lowerCamelCase				)[0].strip()



import re
import string

import numpy as np

import datasets


__UpperCamelCase     		=							'\nReturns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.\n'

__UpperCamelCase     		=							'\nArgs:\n    predictions: List of predicted texts.\n    references: List of reference texts.\n    regexes_to_ignore: List, defaults to None. Regex expressions of characters to\n        ignore when calculating the exact matches. Note: these regexes are removed\n        from the input data before the changes based on the options below (e.g. ignore_case,\n        ignore_punctuation, ignore_numbers) are applied.\n    ignore_case: Boolean, defaults to False. If true, turns everything\n        to lowercase so that capitalization differences are ignored.\n    ignore_punctuation: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\n    ignore_numbers: Boolean, defaults to False. If true, removes all punctuation before\n        comparing predictions and references.\nReturns:\n    exact_match: Dictionary containing exact_match rate. Possible values are between 0.0 and 100.0, inclusive.\nExamples:\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    25.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    50.0\n\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True)\n    >>> print(round(results["exact_match"], 1))\n    75.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["the cat", "theater", "YELLING", "agent007"]\n    >>> preds = ["cat?", "theater", "yelling", "agent"]\n    >>> results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=["the ", "yell", "YELL"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n    >>> print(round(results["exact_match"], 1))\n    100.0\n\n    >>> exact_match = datasets.load_metric("exact_match")\n    >>> refs = ["The cat sat on the mat.", "Theaters are great.", "It\'s like comparing oranges and apples."]\n    >>> preds = ["The cat sat on the mat?", "Theaters are great.", "It\'s like comparing apples and oranges."]\n    >>> results = exact_match.compute(references=refs, predictions=preds)\n    >>> print(round(results["exact_match"], 1))\n    33.3\n\n'

__UpperCamelCase     		=							'\n'







@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION       ,						_KWARGS_DESCRIPTION   )
class 							lowerCamelCase__						(				datasets.Metric   ):






			"""simple docstring"""






			def 					snake_case__					(				self  ):

						'''simple docstring'''


						return datasets.MetricInfo(
						    description=_DESCRIPTION   ,    citation=_CITATION   ,    inputs_description=_KWARGS_DESCRIPTION   ,    features=datasets.Features(
						        {
						            "predictions": datasets.Value("string"   ,    id="sequence"  ),
						            "references": datasets.Value("string"   ,    id="sequence"  ),
						        }  )   ,    reference_urls=[]   ,    )







			def 					snake_case__					(				self   ,    snake_case   ,    snake_case   ,    snake_case=None   ,    snake_case=False   ,    snake_case=False   ,    snake_case=False   ,    ):

						'''simple docstring'''


						if regexes_to_ignore is not None:
									for s in regexes_to_ignore:
												UpperCamelCase__       =							np.array([re.sub(snake_case   ,    ""   ,    snake_case  ) for x in predictions]  )
												UpperCamelCase__       =							np.array([re.sub(snake_case   ,    ""   ,    snake_case  ) for x in references]  )
						else:
									UpperCamelCase__       =							np.asarray(snake_case  )
									UpperCamelCase__       =							np.asarray(snake_case  )

						if ignore_case:
									UpperCamelCase__       =							np.char.lower(snake_case  )
									UpperCamelCase__       =							np.char.lower(snake_case  )

						if ignore_punctuation:
									UpperCamelCase__       =							string.punctuation.maketrans(""   ,    ""   ,    string.punctuation  )
									UpperCamelCase__       =							np.char.translate(snake_case   ,    table=snake_case  )
									UpperCamelCase__       =							np.char.translate(snake_case   ,    table=snake_case  )

						if ignore_numbers:
									UpperCamelCase__       =							string.digits.maketrans(""   ,    ""   ,    string.digits  )
									UpperCamelCase__       =							np.char.translate(snake_case   ,    table=snake_case  )
									UpperCamelCase__       =							np.char.translate(snake_case   ,    table=snake_case  )

						UpperCamelCase__       =							predictions == references

						return {"exact_match": np.mean(snake_case  ) * 100}


